Improving the core resilience of real-world hypergraphs-Reference-Cited by-同舟云学术

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Improving the core resilience of real-world hypergraphs

Published:2023-08-09 Issue:6 Volume:37 Page:2438-2493
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Do Manh Tuan,Shin Kijung^ORCID

Funder

National Research Foundation of Korea

Institute of Information and Communications Technology Planning & Evaluation of Korea

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s10618-023-00958-0.pdf

Reference58 articles.

1. Akoglu L, Faloutsos C (2013) Anomaly, event, and fraud detection in large network datasets. In: Proceedings of the 6th ACM international conference on web search and data mining (WSDM), pp 773–774, https://doi.org/10.1145/2433396.2433496

2. Amburg I, Veldt N, Benson A (2020) Clustering in graphs and hypergraphs with categorical edge labels. In: Proceedings of the web conference 2020 (WWW), pp 706–717. https://doi.org/10.1145/3366423.3380152

3. Aridhi S, Brugnara M, Montresor A, et al (2016) Distributed k-core decomposition and maintenance in large dynamic graphs. In: Proceedings of the 10th ACM international conference on distributed and event-based systems (DEBS), pp 161–168. https://doi.org/10.1145/2933267.2933299

4. de Arruda GF, Petri G, Moreno Y (2020) Social contagion models on hypergraphs. Phys Rev Res 2(2):023,032. https://doi.org/10.1103/PhysRevResearch.2.023032

5. Benson AR, Abebe R, Schaub MT, et al (2018a) Simplicial closure and higher-order link prediction. Proc Natl Acad Sci 115(48):E11,221–E11,230. https://doi.org/10.1073/pnas.1800683115doi:

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